1. Field of Art
The invention generally relates to computer vision, and more specifically, to an online regression process for computer vision applications.
2. Description of the Related Art
Learning regression functions from data is an important problem in machine learning and computer vision with numerous applications. Particularly, kernel machines using Gaussian Processes have demonstrated success in learning nonlinear mappings between high dimensional data and their low dimensional representations. For example, in pose estimation applications, a regression model is learned from a sequence of training images having subjects with known pose configurations. Once learned, the regression model can be applied to input images to generate pose estimations based on the training data. Regression models can further be applied to object tracking problems or other related computer vision applications.
A problem with traditional regression processes is that the computation for learning the regression function does not scale linearly with the number of training data points. Rather, using traditional techniques, learning the regression model is O(n3) in complexity, where n is the size of the training data set. Therefore, such processes can be become computationally burdensome for applications with large datasets. Furthermore, using traditional processes, it may be computationally unfeasible to provide online updates to the regression model. Such online updates refine the regression model based on the current input images and output predictions and can significantly improve the accuracy of the regression.
Several efforts have previously been made to reduce the computational complexity of the regression learning. Examples of such efforts are described in Snelson, E. and Ghahramani, Z.: “Gaussian Processes for Machine Learning” MIT Press (2006); Csato, L. and Opper, M.: “Sparse Online Gaussian Processes”, Neural Computation 14 (2002) 641-669; and Quinonero-Candela, J., Rasmussen, C., and Williams, C.: “Approximation Methods for Gaussian Process Regression” In: Large-Scale Kernel Machines. MIT Press (2007) 203-224, the contents of which are all incorporated by reference herein in their entirety. However, each of these approaches involves an approximation to the regression process that decreases the accuracy of the estimation. These traditional techniques are unable to reduce computation enough to allow for real-time online updates to the regression model while maintaining sufficient accuracy for complex applications such as pose estimation and object tracking. Therefore, what is needed is an improved system and method for fast and accurate regression learning using online updates.